Model generation in SLX using CMSD and XML stylesheet transformations. - In: Proceedings of the 2012 Winter Simulation Conference (WSC), ISBN 978-1-4673-4779-2, (2012), insges. 11 S.
This article introduces a novel methodology for automatic simulation model generation. The methodology is based on the usage of XML stylesheet transformations for generating the actual source code of the target simulation system. It is therefore especially well-suited for all language-based simulation systems. The prerequisite for using the methodology is an appropriate representation of the system under investigation in the Core Manufacturing Simulation Data (CSMD) format. The applicability of our methodology is demonstrated for the simulation language SLX as well as for the visualization system Proof Animation.
Using protocol state machines to support simulation-based emulation projects. - In: Modelling and Simulation 2012, ISBN 978-90-77381-73-1, (2012), S. 234-238
Emulation is used to offer a simulated material flow system for the process of control software development and commissioning. The integration of Manufacturing Execution Systems (MES) with an emulation model is an error prone process and involves multiple stakeholders, including emulation engineers and control engineers. Typically, there is no complete formal description of the interface communication between the MES and the emulation model. This article suggests the use of protocol state machines to firstly formally describe the interface communication and secondly analyze emulation experiments based on the log files of the involved systems. The article further presents a case study and a prototype which have successfully applied the concept of protocol state machines.
Generation of alternatives for model predictive control in manufacturing. - In: I3M 2012 conference proceedings, (2012), S. 7-16
Manufacturing systems are dynamic systems which are influenced by various disturbances or frequently changing customer requests. A continuous process of decision making is required. Model Predictive Control is a common model-based approach for control but needs adaption to be applicable to discrete-event simulation. In this paper we introduce an approach to model and generate non trivial control options and decisions often made in the operation of manufacturing systems. We also show how complex scenarios can be generated. To support a wide-range of applications our approach is based on the core manufacturing simulation data (CMSD) information model. We implement the design and generation of complex scenarios by processing and combining modeled control options. By using our approach, which also applicable to decision support systems, we can enable model-based closed-loop control based on a symbiotic simulation system and automated model generation and initialization.
Facilitating emulation project analysis through the use of protocol state machines. - In: WSC '12 goes Europe!, ISBN 978-1-4673-4780-8, (2012), insges. 2 S.
Emulation is a well-established technology which supports the software development and the commissioning phase of control systems by connecting the real control system with a simulated material flow system. This article suggests the use of protocol state machines to firstly formally describe the interface communication and secondly analyze emulation experiments.
Generation of alternatives for model predictive control in manufacturing systems. - In: WSC '12 goes Europe!, ISBN 978-1-4673-4780-8, (2012), insges. 2 S.
This article discusses possibilities for the generation of alternatives for model predictive control of manufacturing systems.
Bridging the gap: a standards-based approach to OR/MS distributed simulation. - In: ACM transactions on modeling and computer simulation, ISSN 1558-1195, Bd. 22 (2012), 4, S. 18:1-18:23
In Operations Research and Management Science (OR/MS), Discrete Event Simulation (DES) models are typically created using commercial off-the-shelf simulation packages (CSPs) such as AnyLogic, Arena, Flexsim, Simul8, SLX, Witness, and so on. A DES model represents the processes associated with a system of interest. Some models may be composed of submodels running in their own CSPs on different computers linked together over a communications network via distributed simulation software. The creation of a distributed simulation with CSPs is still complex and typically requires a partnership of problem owners, modelers, CSP vendors, and distributed simulation specialists. In an attempt to simplify this development and foster discussion between modelers and technologists, the SISO-STD-006-2010 Standard for COTS Simulation Package Interoperability Reference Models has been developed. The standard makes it possible to capture interoperability capabilities and requirements at a DES modeling level rather than a computing technical level. For example, it allows requirements for entity transfer between models to be clearly specified in DES terms (e.g. the relationship between departure and arrival simulation times and input element (queue, workstation, etc.), buffering rules, and entity priority, instead of using specialist technical terminology. This article explores the motivations for distributed simulation in this area, related work, and the rationale for the standard. The four Types of Interoperability Reference Model described in the standard are discussed and presented (A. Entity Transfer, B. Shared Resource, C. Shared Event, and D. Shared Data Structure). Case studies in healthcare and manufacturing are given to demonstrate how the standard is used in practice.
A comparison of the CSPI and CMSD standards. - In: Spring Simulation Interoperability Workshop 2012 (2012 Spring SIW), ISBN 978-1-618-39719-5, (2012), S. 82-89
The SISO standards SISO-STD-006-2010 and SISO-STD-008-2010 are two standards focusing on the interoperability requirements of the manufacturing and logistics domains. In these domains, simulation studies are mostly performed using commercial-off-the-shelf (COTS) Simulation Packages (CSPs), such as Arena , Anylogic , Flexsim , Plant Simulation , Simul8 , SLX , Witness , etc. As both standards seem to address similar problems, namely interoperability issues when using such CSPs, we investigate the specific strengths of each of these standards and derive recommendations for their usage. In specific, we illustrate how the standard for Commercial Off-the-Shelf (COTS) Simulation Package Interoperability (CSPI) Reference Models (SISO-STD-006-2010) can facilitate interoperability between different CSPs. A use case from the commercial vehicle sector is presented in support of this discussion. Further, we illustrate how the standard for Core Manufacturing Simulation Data (SISO-STD-008-2010) can be used to exchange data between other information systems in manufacturing and CSPs. We outline a use case in which CMSD is used as the basis for simulation model generation in Plant Simulation. We also discuss the usage of CMSD to carry model initialization data and its potential to exchange data between different CSPs.
Virtuelle Produktionsabsicherung am Beispiel Montage Powertrain. - In: Digitales Engineering und virtuelle Techniken zum Planen, Testen und Betreiben technischer Systeme, (2011), S. 111-119
Die Automobilindustrie ist derzeit weltweit mit einer steigenden Nachfrage und wachsenden Absatzmärkten konfrontiert. Die zusätzlich turbulente Marktlage führt zu einem Konkurrenzkampf um wichtige Marktanteile zwischen den Automobilherstellern, welche sich mehr denn je in einem Spannungsfeld aus den Faktoren Qualität, Produktivität und Kosten bewegen. Der sich daraus ergebende Wettbewerb hat kürzere Innovations- und Produktlebenszyklen zur Folge. Die Anforderungen der Kunden unterscheiden sich zusätzlich von Markt zu Markt immer mehr - und führen folgend zu einer steigenden Variantenanzahl. Die durchschnittliche Anzahl der Serienanläufe pro Jahr hat sich beispielsweise bei dem deutschen Automobilhersteller Daimler AG in den letzten 20 Jahren mehr als verdreifacht. Empirische Studien zeigen zudem, dass ein Großteil der notwendigen Änderungsmaßnahmen erst ab der Nullserienphase und somit nach Erstellung von Serienwerkzeugen bzw. während der Phase des Serienanlaufs durchgeführt wird. Effiziente Produktionsanläufe gewinnen somit zunehmend an Bedeutung. Der Einsatz von digitalen Methoden und Softwarewerkzeugen entlang des Produktentstehungsprozesses macht es bereits möglich, einen Großteil der Anlaufvorgänge im Vorfeld digital abzusichern. Im Folgenden wird ein Workflow zur virtuellen Produktionsabsicherung dargestellt und dessen Anwendung im Aggregatebereich bei Daimler Truck erläutert.
Initialization of simulation models using CMSD. - In: Proceedings of the 2011 Winter Simulation Conference, ISBN 978-1-457-72106-9, (2011), S. 2228-2239
In the context of online- and symbiotic simulation, the precise initialization of simulation models based on the state of the physical system is a fundamental requirement. In these simulations, the simulation model typically serves as an operational decision support tool. Obviously, it can therefore not start empty and idle. The accurate capturing of initial conditions is fundamental for the quality of the model based predictions. In literature, it is only generally stated that the simulation model must maintain a close connection with the physical system. Our work systematically investigates which data from the physical system is needed for initialization, how it shall be transferred into the simulation model in a standardized way, and which potential problems must be solved in the simulation system to adequately initialize its model elements. We present a solution based on the core manufacturing simulation data (CMSD) standard, suggest necessary extensions and demonstrate a prototypical implementation.
Distributed computing and modeling & simulation: speeding up simulations and creating large models. - In: Proceedings of the 2011 Winter Simulation Conference, ISBN 978-1-457-72106-9, (2011), S. 161-175
Distributed computing has many opportunities for Modeling and Simulation (M&S). Grid computing approaches have been developed that can use multiple computers to reduce the processing time of an application. In terms of M&S this means simulations can be run very quickly by distributing individual runs over locally or remotely available computing resources. Distributed simulation techniques allow us to link together models over a network enabling the creation of large models and/or models that could not be developed due to data sharing or model reuse problems. Using real-world examples, this advanced tutorial discusses how both approaches can be used to benefit M&S researchers and practitioners alike.